Abstract
Clustering and classification problems arise in a wide range of application settings from clustering documents, placing centers in net- works, to image processing, biometric analysis, language modeling and the categorization of hypertext documents.
The applications mentioned above give rise to a number of related al- gorithms problems, each of which are NP-complete. Approximation al- gorithms provide a framework to develop algorithms for such problems that have provable performance guarantees. In this talk we shall survey some of the general techniques, and recent developments in approxima- tion algorithms for these problems.
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© 1999 Springer-Verlag Berlin Heidelberg
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Tardos, E. (1999). Approximation Algorithms for Some Clustering and Classification Problems. In: Algorithms and Computation. ISAAC 1999. Lecture Notes in Computer Science, vol 1741. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46632-0_19
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DOI: https://doi.org/10.1007/3-540-46632-0_19
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